Our overall goal is to develop computational approaches to elucidate the design principles underlying biological control circuits. Rather than taking a traditional approach of asking how any one circuit works, however, we will take the inverse approach of asking what are all circuit architectures that could perform a particular target function. Our aims are to: 1) Develop algorithms to computationally enumerate and classify core circuit architectures that can robustly achieve a given target function. These methods will involve coarse-grained representations of circuits, which can be efficiently searched and evaluated. 2) Use high resolution circuit analysis methods to identify compatible parameter sets and functional tradeoffs associated with specific architectures. 3) Test the validity the above analysis by using the results as a guide for both design of synthetic circuits (AIM 2) and the identification and classification of natural circuits (AIM 3) that can perform the target function. Our efforts will initially focus on analyzing one testbed target behavior - Perfect Adaptation. Perfect Adaptation is a biologically important sensory function in which a sensing system produces an output spike in response to an input, but restores itself to the initial basal output level if the input is sustained. This adaptation behavior allows responses to small input changes over a wide dynamic range. Subsequently we will apply these approaches to understand and design circuit architectures compatible with other target functions.